add shuhei's suggestion

Author: ravinkohli

autoPyTorch/data/tabular_feature_validator.py

@@ -158,8 +158,8 @@ def _fit(
             if not X.select_dtypes(include='object').empty:
                 X = self.infer_objects(X)
 
-            if len(self.transformed_columns) == 0 and self.feat_type is None:
-                self.transformed_columns, self.feat_type = self._get_columns_to_encode(X)
+            self.transformed_columns, self.feat_type = self._get_columns_to_encode(X)
+
             assert self.feat_type is not None
 
             if len(self.transformed_columns) > 0:
@@ -326,8 +326,7 @@ def _check_data(
 
             # Define the column to be encoded here as the feature validator is fitted once
             # per estimator
-            if len(self.transformed_columns) == 0 and self.feat_type is None:
-                self.transformed_columns, self.feat_type = self._get_columns_to_encode(X)
+            self.transformed_columns, self.feat_type = self._get_columns_to_encode(X)
 
             column_order = [column for column in X.columns]
             if len(self.column_order) > 0:
@@ -369,6 +368,10 @@ def _get_columns_to_encode(
             feat_type:
                 Type of each column numerical/categorical
         """
+
+        if len(self.transformed_columns) > 0 and self.feat_type is not None:
+            return self.transformed_columns, self.feat_type
+
         # Register if a column needs encoding
         transformed_columns = []